Emergence of cooperation: A reputation-modulated reinforcement learning
- URL: http://arxiv.org/abs/2608.20016v1
- Date: Thu, 20 Aug 2026 13:27:41 GMT
- Title: Emergence of cooperation: A reputation-modulated reinforcement learning
- Abstract summary: Reputation is widely recognized as a key mechanism for sustaining cooperation.<n>We propose a spatial prisoner's dilemma game grounded in the reinforcement learning paradigm.<n>Our results reveal that reputation-modulated learning significantly promotes the emergence of cooperative behavior.
- Score: 19.357969561684982
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Reputation is widely recognized as a key mechanism for sustaining cooperation. However, most existing game-theoretic models treat reputation primarily as an external factor that modulates payoffs, interaction structures, or strategy update rules. In many social contexts, though, reputation operates primarily as information -- it shapes how individuals interpret their own experiences and assess the behavior of others. To bridge this gap, we propose a spatial prisoner's dilemma game grounded in the reinforcement learning paradigm, in which agents equipped with Q-learning integrate both individual and social information via a locally defined reputation metric to guide their decisions. Our results reveal that reputation-modulated learning significantly promotes the emergence of cooperative behavior, and we observe a discontinuous phase transition from full cooperation to full defection as the temptation increases. Cooperation spreads through the nucleation of cooperative clusters, whereas the disintegration of these clusters drives the system into an absorbing state of complete defection. Overall, this study demonstrates that reputation facilitates cooperation not only by providing direct incentives but also by reshaping the social information landscape that agents rely on for learning and adaptation.
Related papers
- Social Hippocampus Memory Learning [54.381608978482184]
SoHip is a memory-centric social machine learning framework.<n>It enables collaboration among heterogeneous agents via memory sharing rather than model sharing.<n>Experiments on two benchmark datasets with seven baselines demonstrate that SoHip consistently outperforms existing methods.
arXiv Detail & Related papers (2026-03-26T16:31:00Z) - Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning [50.56947843548702]
We propose a decentralized dynamic cooperation framework for Federated continual learning.<n>Clients establish dynamic cooperative learning coalitions to balance the acquisition of new knowledge and the retention of prior learning.<n>We also propose a merge-blocking algorithm and a dynamic cooperative evolution algorithm to achieve cooperative and dynamic equilibrium.
arXiv Detail & Related papers (2025-09-28T06:53:23Z) - Corrupted by Reasoning: Reasoning Language Models Become Free-Riders in Public Goods Games [87.5673042805229]
How large language models balance self-interest and collective well-being is a critical challenge for ensuring alignment, robustness, and safe deployment.<n>We adapt a public goods game with institutional choice from behavioral economics, allowing us to observe how different LLMs navigate social dilemmas.<n>Surprisingly, we find that reasoning LLMs, such as the o1 series, struggle significantly with cooperation.
arXiv Detail & Related papers (2025-06-29T15:02:47Z) - Beyond the Tragedy of the Commons: Building A Reputation System for Generative Multi-agent Systems [14.839510542470734]
RepuNet is a dual-level reputation framework that models both agent-level reputation dynamics and system-level network evolution.<n>We show that RepuNet effectively mitigates the 'tragedy of the commons', promoting and sustaining cooperation in generative MASs.
arXiv Detail & Related papers (2025-05-08T08:02:20Z) - Multi-agent cooperation through learning-aware policy gradients [53.63948041506278]
Self-interested individuals often fail to cooperate, posing a fundamental challenge for multi-agent learning.<n>We present the first unbiased, higher-derivative-free policy gradient algorithm for learning-aware reinforcement learning.<n>We derive from the iterated prisoner's dilemma a novel explanation for how and when cooperation arises among self-interested learning-aware agents.
arXiv Detail & Related papers (2024-10-24T10:48:42Z) - Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games [47.8980880888222]
Multi-agent scenarios often involve mixed motives, demanding altruistic agents capable of self-protection against potential exploitation.<n>We propose LASE Learning to balance Altruism and Self-interest based on Empathy.<n>LASE allocates a portion of its rewards to co-players as gifts, with this allocation adapting dynamically based on the social relationship.
arXiv Detail & Related papers (2024-10-10T12:30:56Z) - Reciprocal Reward Influence Encourages Cooperation From Self-Interested Agents [2.1301560294088318]
Cooperation between self-interested individuals is a widespread phenomenon in the natural world, but remains elusive in interactions between artificially intelligent agents.<n>We introduce Reciprocators, reinforcement learning agents which are intrinsically motivated to reciprocate the influence of opponents' actions on their returns.<n>We show that Reciprocators can be used to promote cooperation in temporally extended social dilemmas during simultaneous learning.
arXiv Detail & Related papers (2024-06-03T06:07:27Z) - Emergent Cooperation under Uncertain Incentive Alignment [7.906156032228933]
We study how cooperation can arise among reinforcement learning agents in scenarios characterised by infrequent encounters.
We study the effects of mechanisms, such as reputation and intrinsic rewards, that have been proposed in the literature to foster cooperation in mixed-motives environments.
arXiv Detail & Related papers (2024-01-23T10:55:54Z) - Interruption-Aware Cooperative Perception for V2X Communication-Aided
Autonomous Driving [49.42873226593071]
We propose V2X communication INterruption-aware COoperative Perception (V2X-INCOP) for V2X communication-aided autonomous driving.
We use historical cooperation information to recover missing information due to the interruptions and alleviate the impact of the interruption issue.
Experiments on three public cooperative perception datasets demonstrate that the proposed method is effective in alleviating the impacts of communication interruption on cooperative perception.
arXiv Detail & Related papers (2023-04-24T04:59:13Z) - A mechanism of Individualistic Indirect Reciprocity with internal and
external dynamics [0.0]
This research proposes a new variant of Nowak and Sigmund model, focused on agents' attitude.
Using Agent-Based Model and a Data Science method, we show on simulation results that the discriminatory stance of the agents prevails in most cases.
The results also show that when the reputation of others is unknown, with a high obstinacy and high cooperation demand, a heterogeneous society is obtained.
arXiv Detail & Related papers (2021-05-28T23:28:50Z) - Cooperation and Reputation Dynamics with Reinforcement Learning [6.219565750197311]
We show how reputations can be used as a way to establish trust and cooperation.
We propose two mechanisms to alleviate convergence to undesirable equilibria.
We show how our results relate to the literature in Evolutionary Game Theory.
arXiv Detail & Related papers (2021-02-15T12:48:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.